Assessment of Heavy Metal Distributions in Sand Beaches in the Maltese Islands

Abstract

This study focuses on the quantification of heavy metals present in 18 sand beaches on the islands of Malta and Gozo. A total of 134 samples were collected, digested using Aqua Regia, and analysed using flame AAS to find the concentration of six heavy metals. Concentrations obtained in descending order are: Sr > Fe > Mn > Pb > Zn > Cu. Using PCA, Fe and Mn resulted as homogenous distributions with a probable prevalent lithogenic origin. Pb is possibly dominantly anthropogenic, while Cu and Zn are of a mixed nature. Cluster analysis was used to prove the interaction between concentrations and different bays from where the samples were gathered. This showed that Ballut Reserve Bay and Rinella Bay in Malta and Marsalforn Bay in Gozo are amongst the bays most affected by heavy metal content. It has been observed that bays with higher heavy metal content lie in the same zones with the highest geological wear rate induced by sea waves. Health risk assessment undertaken for adults and children shows negligible effects of non-carcinogenic risk and cancer risk indices. Potential ecological risk computed for the concentrations obtained showed considerable Cu risk and a moderate Pb risk at the bays analysed, none of which are contaminated with these elements.

1. Introduction

Rocks on the Maltese islands are of the marine carbonate shallow water type, which match those found in some areas on the neighbouring island of Sicily [1]. Sand mostly forms by the erosion of exposed rock from valleys leading up to the beaches. As outlined by Lang in 1960, the majority of exposed formations comprise a combination of Globigerina Limestone, Blue Clay, and Greensand formations [2], with Greensand being the most common occurrence. Such formations are soft materials, weather fast, and determine the sand colour [3], and mainly depend on the composition of the geological strata in the location and the number of tiny crustacean shells that these have originated from. As reported by Gauci et al. (2005) and Cassar and Stevens (2002), touristic endeavours taken up by the island have impeded the natural regeneration of sand, causing the loss of geomorphological activity in most sandy beaches [4,5].

The major component of sand is calcium carbonate which constitutes approximately 20% of its biogenic source [6]. Contamination of sand beaches occurs also from surface soil from neighbouring arable land which tends to be transported down valleys leading up to the beaches during precipitation seasons. Additives added to the soil to enhance its properties, namely fertilizers and pesticides which include heavy metals, are soluble in rainwater resulting in the accumulation of heavy metal concentrations in sandy beaches. According to Batista et al. sandy beaches generally have higher concentrations of heavy metals than soils as they infiltrate into the sand via multiple sources including water and silt [7]. The presence of heavy metals in surface soil are linked to human activity in the area, as proven by the study by Coᶊkun et al. (2016) which determined the presence of heavy metals in the surface soil in the Thrace region in Turkey [8]. Results showed that the majority of the heavy metals studied were present in the zones with most traffic and industrial areas of the region, thus contributing to the anthropogenic sources of such elements [8].

Similar relationships were obtained in an Algerian study by Benhaddya and Hadjel (2013) showing that the concentrations of heavy metals are higher in areas of higher industrial and human development, owing to the anthropogenic effect of increased industrial activities, traffic emissions, leaching of polluting elements from improper waste disposal, and dwelling zones [9].

Similar results have been shown in our previous work undertaken about heavy metal content in agricultural soil in Malta. Links of heavy metal concentrations in the soil to the source of contamination were made, proving both the lithogenic and anthropogenic effects on toxicity concentrations upon their variations according to the plant growth stage [10]. Another study of local Maltese soil contamination by heavy metals proved that all heavy metals studied were present in Marsaxlokk, Qrendi, Gharghur, and Zabbar in Malta and Ghajnsielem in Gozo [11]. Briffa also reported that Al was the most abundant heavy metal in all districts studied, presenting 98% of the total heavy metal concentrations, followed by Mn, Pb, and Cu [11].

Heavy metal concentrations have been recorded also in silt and sand particles on riverbeds, which originate as agriculture chemicals, industrial effluent, fireworks remains, and improper drainage systems, which are slowly transported via rain and water currents to river estuaries along with other sedimentary particles. Bioaccumulation of such heavy metal deposits in natural environments may lead to excess concentrations surpassing limit values, unbalancing the natural environmental equilibrium.

Ionic compounds in the form of soluble salts which separate due to their interaction with a solvent, such as sodium chloride, are among the major carriers of heavy metals, as these are more likely to be carried by water. Geologically in Malta, there is a substantial amount of limestone in the strata which hold soluble salts that are soluble in water [12]. Thus, the presence of limestone zones along the Maltese coastline is a possible source contributing to the presence of heavy metals in beaches due to water runoff. Coastal currents aid in the redistribution of contaminants added anthropogenically in the water, adding to the concentration of heavy metals deposited on sand beaches [13]. As reported by Wuana and Okieimen, heavy metals are filtered by the soil composition into separate distributions [14]. Such adsorption reactions harm both the natural properties of sand and those organisms that depend on it, which may result in it being difficult and expensive to remediate and reduce. Lithogenic sources of heavy metals may include the natural bedrock since different strata are abundant in different minerals.

The contamination of sand and other sediments with heavy metals has harmful consequences for humans and organisms and is thus of high importance when studying environmental pollution. Sediments are abundant in metals in an aquatic environment and thus provide a clear indication of the level of pollution of the water [15]. Sediments can act as both sources and sinks of pollutants [13,15]. Sediments release heavy metals through deposition, adsorption, and desorption processes which are affected by physiochemical processes that include the temperature, flow conditions, organic matter and microbial content, water salinity, pH, and particle size [15]. High acidity levels in sediments increase the dissolution of metal complexes by the release of metal ions in the water, hence posing a greater ecological risk towards the benthos [15]. Many chemical changes take place in the sediment–water boundary; hence, the study of geochemical compositions of sand is effective in environmental studies.

The aim of this study is to record and study heavy metal concentrations in various sand beaches around the Maltese islands to understand environmental pollution and water quality. Little information is currently available about toxicity levels in the natural environment which is highly frequented by locals and tourists in Malta and Gozo. Water quality and the effect of anthropogenic sources is highly studied; thus, this study offers a counterbalance and may further support such knowledge. This study focuses on the distribution of heavy metal concentrations around the islands since these may pose detrimental effects to human health which may also negatively affect environmental sustainability. The heavy metals studied at each of the 18 beaches located around Malta and Gozo include Cu, Fe, Pb, Mn, Sr, and Zn. These six elements have been chosen according to their concentration abundancy upon carrying out preliminary tests.

In-depth statistical analysis over the 134 samples collected that were worked in triplicate was undertaken to assess the level of contamination of Maltese beaches together with their variability in different zones. Multivariate analysis and principal component analysis were performed over the concentrations obtained to classify the heavy metals studied according to their possible anthropogenic and lithogenic sources.

2. Materials and Methods

2.1. Study Area Location

Despite Malta being a small island, it is distinguished by its relatively long coastline which approximates 272 km [5]. About 90.5% of the coastline is defined by rocky shores, 2.4% are attributed to sandy and shingle shores, and the remaining 7% constitutes a built-up environment [5].

Given that the Maltese islands were long ago submerged, the number of biogenic factors vary according to the time period in which the strata formed [6]. According to Gatt, biogenic sand is derived from molluscs, scleractinian coral, and calcareous green algae, with the same presence found in underwater dome features, as published by Savini et al. [16,17]. The biogenic accumulation produces cherts including silica rocks, which have been reported to be extensively located around Malta. Solai et al. [13] noted that calcareous sediments are characteristic of low latitudes, while calcareous skeletal sand is prominent at altitudes closer to 60°. The Maltese islands lie on a latitude of 35°55′4.7028″ N; hence, calcareous sediments are predominant.

Coralline red algae, corals, echinoids, and foraminifera have been found in the Lower Coralline Limestone in high quantities, and most of these have been exposed to natural processes which have partially or completely degraded their initial form [18]. Schembri et al. reported that the coarseness between Coralline Limestone and Globigerina Limestone varies because of such degradative changes [19]. Schembri et al. also noted that biota tend to grow in the coarser Coralline rock [19], thus explaining the relationship between the density of the Coralline and its hardness properties which make it increasingly harder than its Globigerina counterpart.

A total of 18 beaches were chosen, with 14 well distributed in Malta and 4 in Gozo.

Table 1. List of sand beaches studied and their corresponding number of collected samples.

Location Details	Location Name	Location Coordinates	Number of Samples	Approximate Bay Area (m2)
Malta	Armier Bay	35°59′20.52″ N 14°21′23.39″ E	9	3157
	Ballut Reserve Bay	35°50′20.69″ N 14°32′53.13″ E	5	1830
	Ghadira Bay	35°58′15.10″ N 14°20′59.25″ E	27	9198
	Gnejna Bay	35°55′13.30″ N 14°20′35.87″ E	10	3491
	Golden Bay	35°56′1.72″ N 14°20′39.72″ E	14	5075
	Little Armier Bay	35°59′21.97″ N 14°21′34.04″ E	4	1560
	Mistra Bay	35°57′28.57″ N 14°23′23.12″ E	2	722
	Paradise Bay	35°58′54.94″ N 14°19′56.55″ E	4	1556
	Pretty Bay	35°49′30.10″ N 14°31′45.77″ E	14	5012
	Rinella Bay	35°53′36.31″ N 14°31′39.91″ E	3	927
	Riviera Bay	35°55′44.60″ N 14°20′41.28″ E	10	3575
	St. George’s Bay	35°49′55.40″ N 14°31′50.24″ E	4	1201
	Xemxija Bay	35°56′48.18″ N 14°22′59.30″ E	1	401
	White Tower Bay	35°59′32.27″ N 14°21′55.61″ E	7	2601
Gozo	Hondoq ir-Rummien Bay	36°1′40.59″ N 14°19′19.31″ E	2	770
	Ramla Bay	36°3′40.99″ N 14°17′2.58″ E	16	5807
	Xlendi Bay	36°1′49.88″ N 14°17′2.58″ E	1	203
	Marsalforn Bay	36°4′17.4108″ N 14°15′37.3788″ E	1	573
Total			134	47,659

below provides details of the sampling locations and the corresponding number of samples collected from each beach. The Supplementary Figure S1 included illustrates the geographical locations of the beaches reviewed and a distribution of the heavy metal concentrations obtained. Supplementary Figure S2 provides more detail of the locations of the beaches studied by illustrating aerial views of each beach.

2.2. Sampling and Pre-Treatment

In all, 134 samples were collected. The number of samples per location was determined as a ratio of the area of each beach to the total area of all beaches considered, as shown in Table 1. Samples were collected using the line-transect method along the length of each beach. For conformity in sampling procedure, each transect was taken between 1 m and 10 m away from the sea-line, depending on the width of the beach, while a 12 m distance was kept between sampling points. Samples were collected in February 2021, when 7.8 mm of rainfall was recorded and an average temperature and humidity level of 14 °C and 79% were documented, respectively. Samples were collected from the surface using plastic tools and stored in well-labelled plastic bags for reduced contamination. Each sample was oven dried at 70 °C for approximately 48 h until obtaining constant sample mass. The dry samples were then sieved using a 2 mm sieve to remove large debris and were stored in tightly closed glass jars together with a silica gel packet to reduce the reabsorption of water content from ambient humidity.

2.3. Chemical Analysis

Glassware used throughout the chemical analysis process was cleaned with soap and water and soaked in an aqua regia acid bath for more than 24 h prior to its use. The aqua regia acid bath was made using 3:1 part hydrochloric acid and nitric acid of the same grades as used for the digestion process, diluted with 1 part water. A sample mass of 5 mg was weighed using an analytical balance and placed in clean labelled culture tubes, with triple repeatability; thus, a total of 402 samples were to be analysed. Digestion was performed with aqua regia solution mixed with hydrochloric acid with 37% concentration RPE ACS-Reag.PH.EUR.-Reag. USP grade and nitric acid with 69.5% concentration RPE ACS-Reag.PH.EUR.-Reag. USP grade using a molar ratio of 3:1. Aqua regia solution was poured gradually in steps of 1 mL until a total of 10 mL was added to ensure no sample mass was lost during the vigorous reaction between the sand and acid solution. For optimum control of the gaseous reaction, an air pump was used to blow air inside the mixture to reduce the amount of bubbling produced. Since the reaction was then subdued, the digestion solution was then added in steps of 2 mL and 3 mL, respectively. until a maximum of 15 mL of solution was added. The volume was then topped off to reach 25 mL in total. Digestion was performed at 105 °C for 24 h in heating blocks to increase acid effectiveness and to provide uniform heating for digestion. Sample solutions in their respective culture tubes were allowed to cool to room temperature before vacuum filtering using a sintered funnel of pore size index of 160–250 µm and ashless filter paper for fast removal of large debris. Sample volumes were adjusted to 100 mL using an adequate amount of deionised distilled water and stored in a cool dry place in sterile containers made of perflouroalkoxy (PFA) polymers to reduce analyte absorption by the container material.

2.4. Heavy Metals Analysis Using Spectroscopy

Upon digestion of the sand samples collected for this study, atomic absorption spectroscopy (AAS) was used to analyse the content of six heavy metals present in the sand. This is achieved by measuring the concentration of metals in the material according to the amount of light being emitted at a specific wavelength that matches the element under review. AAS can produce measurements with an accuracy in the range of 0.5% to 5% [19]. Contamination of equipment and storage used are likely sources of error; hence, the preservation of pH and trace elements in solution is imperative. Samples were stored in a cool environment in perflouroalkoxy (PFA) sterile containers for a short period of time to keep conditions stable during analysis. Several method blanks were produced using the same process used for sample processing.

The atomic absorption spectrophotometer used was a continuous flame-type AA-7000 model produced by Shimadzu Europa GmbH. Lamp mode BGC-D2 setting was used for testing Cu, Fe, Pb, Mn, and Zn, with a non-BGC lamp setting used to read Sr concentrations. Burner height was set to 7 mm for all elements tested except for Fe, where burner height was set to 9 mm. Burner lateral and burner angle were set at 0 pulse and 0 degrees, respectively, and supply flow rate for the air-acetylene flame used was set in the range of 1.6–2.8 L/min. Wavelengths emitted by the respective lamps used included 324.8 nm for Cu, 248.3 nm for Fe, 283.3 nm for Pb, 279.5 nm for Mn, 460.7 nm for Sr, and 213.9 nm for Zn.

Absorbance values were obtained for all samples prepared for analysis for all elements under review. Calibration curves plotting absorbance against concentration were created using the standard addition method and sample analyte solution was used to control matrix effects in the results. Concentrations computed were checked using percentage spiked recovery analysis to ensure matrix effects were quantified. Calibration checks were performed throughout the analysis approximately every 10 sample readings. It was ensured that the Percentage Relative Standard Deviation (% RSD) was less than 2% for readings collected by preparing standards of the same concentration to evaluate precision in results.

2.5. Statistical Analysis

Software used for the statistical analysis included Microsoft Excel, IBM SPSS Statistics 24, and JMP Statistical Software. Descriptive statistics, correlation analysis, normality tests using a 95% confidence interval, multivariate analysis, principal component analysis (PCA), and factor analysis (FA) were applied to analyse the data. Correlations between beach location and heavy metal concentrations were analysed together with possible sources of heavy metals found on beaches in Malta and Gozo.

2.6. Health Risk Assessment

Long-term human exposure to heavy metals and toxins in the environment leads to various negative health effects. Such heavy metals may enter the human body through inhalation, dermal contact, and ingestion; the latter two are the most relevant to possible toxicity from sand beaches. These health risks are evaluated by several parameters including the Hazard Quotient (HQ) and the Hazard Index (HI), which are dependent on the Average Daily Dose (ADD) and the Reference Dose (RFD) for each heavy metal studied. RFD signifies the reference dose for each heavy metal that an individual is exposed to per day, either by ingestion or dermal contact, throughout his entire life without it causing any harm. ADD values, HQ values, and HI are computed using the equations shown below, with parameters used to compute such values included in

Table 2. Parameter values used to compute average daily dose by heavy metal ingestion and dermal contact [20,21,22,23,24,25].

Parameter	Symbol	Unit	Adult Value	Children Value
Ingestion Rate	IRingest	mg/day	30	80
Exposure Frequency	EFdefault	days/year	350	350
	EFmodified		41	41
Exposure Duration	EDdefault	years	30	6
Average Body Weight	BW	kg	80	18.6
Non-Carcinogens Averaging Time	AT = ED × 365	days	10,950	2190
Carcinogens Averaging Time	ATdefault = 70 years × 365	days	25,550	25,550
	ATchildren-modified = 6 years × 365	days	Not modified	2190
Unity Conversion Factor	CF	kg/mg	1 × 10−6	1 × 10−6
Exposed Skin Area	SA	cm2	19,960	6820
Average Adherence Factor	AF	mg/cm2	0.352	4.48
Dermal Absorption Fraction	ABS	/	0.001	0.001

and

Table 3. RFD and SF values for heavy metals [20,21,22,23,24,25,26,27,28,29,30].

Metal	RFD 1 by Ingestion (mg/kg/day)	RFD 1 by Dermal Contact (mg/kg/day)	Slope Factor by Ingestion (mg/kg/day)	Slope Factor by Dermal Contact (mg/kg/day)
Fe	0.700	Not found	Not found	Not found
Mn	0.140	Not found	Not found	Not found
Zn	0.300	0.060	Not found	Not found
Pb	3.5 × 10−3	5.25 × 10−4	8.5 × 10−3	5.675 × 10−3 *
Cu	0.040	0.012	Not found	Not found
Sr	0.6	Not found	8.9 × 10−10	Cannot be computed

¹ RFD is the reference dose for each heavy metal. * Computed through Equations (5) and (6).

, respectively. Adverse non-carcinogenic effects are characterised by values of HQ > 1, while if HQ < 1, concentrations obtained signify no detrimental effects. If the Hazard Index (HI) < 1, the non-carcinogenic adverse effects are assumed to be negligible [20,21].

$${\mathrm{ADD}}_{\mathrm{ingestion}}=\frac{{\mathrm{C}}_{\mathrm{s}}\times {\mathrm{IR}}_{\mathrm{ingest}}\times \mathrm{EF}\times \mathrm{ED}\times \mathrm{CF}}{\mathrm{BW}\times \mathrm{AT}}$$

(1)

$${\mathrm{ADD}}_{\mathrm{dermal}}=\frac{{\mathrm{C}}_{\mathrm{s}}\times \mathrm{SA}\times \mathrm{AF}\times \mathrm{ABS}\times \mathrm{EF}\times \mathrm{ED}\times \mathrm{CF}}{\mathrm{BW}\times \mathrm{AT}}$$

(2)

$$\mathrm{HQ}=\frac{\mathrm{ADD}}{\mathrm{RFD}}$$

(3)

$$\mathrm{HI}={{\displaystyle \sum }}_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{HQ}}_{\mathrm{i}}$$

(4)

where Cs represents the average heavy metal sample concentration; IR_ingest represents the heavy metals ingestion rate; EF represents the exposure frequency; ED represents the exposure duration; BW represents the average body weight for adults or children; AT represents the averaging time for carcinogens and non-carcinogens; CF is a unity conversion factor; SA represents the estimated average exposed skin area; AF represents the adherence factor; ABS represents the dermal absorption fraction; HI is the overall toxic risk; and n represents the total number of metals under consideration, as stated in the United States Environmental Protection Agency IRIS (USEPA IRIS) (2011) [21], the United States Environmental Protection Agency Exposure Factors Handbook: 2011 Edition [22], and the United States Environmental Protection Agency Update for Chapter 5 of the Exposure Factors Handbook: Soil and Dust Ingestion [23], which are referenced by Anyanwu and Nwachukwu (2010), Agoro et al. (2020), and Liang et al. (2017) [20,24,25] and are shown in Table 2. It should be noted that an adjustment on the EF has been taken from the default values used in standard studies, since in this case persons exposed to heavy metal toxicity from sand beaches do not typically spend a full year at the beach. A typical EF of eight-hour days for four months (summer period) in a year has been approximated, as tabulated below. It should also be noted that the values documented in Table 2 for the Exposed Skin Area include the head, 75% of the body trunk, arms, hands, legs, and feet for adults and children using values documented in the United States Environmental Protection Agency Exposure Factors Handbook: 2011 Edition [23]. AF values for the whole body of adults and children have been averaged out using values specified for different body parts (similarly mentioned for the SA), using the Mean Solid Adherence to Skin for children playing in sediments (tidal flats) and for adults during clamming in tidal flats [23].

Cancer Risk (CR) denotes the incremental risk for a human to face cancer over a lifetime due to the exposure to carcinogenic matter. The exposure is approximated equal to a full day for an estimate of 70 years, which represents an approximate full lifetime for a person [30]. CR is calculated through the product of the parameters Average Daily Dose (ADD) and slope factor (SF), where the slope factor is a value representing an incremental rate of cancer development in humans derived from a lifetime exposure to toxins. SF values used for computations performed are based on USEPA IRIS (2011) standards and EPA (1994) standards, and are outlined in Table 3 [21,27]. Equations used to calculate the slope factor for dermal contact are as follows:

RFD_dermal = RFD_ingestion × ABS_GI

(5)

SF_ingestion = SF_dermal × ABS_GI

(6)

where RFD is the reference dose by ingestion or dermal contact of the metal; ABS_GI is the gastrointestinal absorbance factor; and SF is the slope factor defined for ingestion or dermal contact with the metal [28].

The Cancer Risk Index (RI) is calculated through the summation of CR individual values calculated for each heavy metal in the sand samples analysed [30].

2.7. Potential Ecological Risk Assessment

Assessment of beach pollution originating in sand due to anthropogenic or natural contamination is performed by applying pollution indices to discover the origin of heavy metal concentrations accumulated in the medium being studied. The ecological risk is derived by calculating the geo-accumulation index and provides a good estimate of the extent of heavy metal contamination in the study locations. The geo-accumulation index is calculated using:

$${\mathrm{I}}_{\mathrm{geo}}={\log }_2\left(\frac{{\mathrm{C}}_{\mathrm{n}}}{1.5\times { \mathrm{B}}_{\mathrm{n}}}\right)$$

(7)

where Cn is the metal concentration in the sample for each metal from this study and Bn is the background level for individual metals, taken as 4 ppm, 3800 ppm, 1100 ppm, 9 ppm, 610 ppm, and 20 ppm for Cu, Fe, Mn, Pb, Sr and Zn, respectively [31]. Seven classes of the geo-accumulation index are listed in

Table 4. Classification of geo-accumulation index (I_geo) [32].

Geo-Accumulation Index Level	Igeo Class	Igeo Value
Uncontaminated	0	Igeo ≤ 0
Uncontaminated/moderately contaminated	1	0 < Igeo < 1
Moderately contaminated	2	1 < Igeo < 2
Moderately/strongly contaminated	3	2 < Igeo < 3
Strongly contaminated	4	3 < Igeo < 4
Strongly/extremely contaminated	5	4 < Igeo < 5
Extremely contaminated	6	5 < Igeo

[32].

The Contamination Factor (CF) describes the relationship between the metal concentration and its corresponding background level. The Potential Ecological Risk Index (PERI) measures the degree of heavy metal pollution in sediments depending on the level of toxicity of metals and the effect of such toxins in the environment. The equations used to calculate the relevant parameters include:

$$\mathrm{CF}=\frac{{\mathrm{C}}_{\mathrm{metal}}}{{\mathrm{C}}_{\mathrm{background}}}$$

(8)

$${\mathrm{E}}_{\mathrm{i}}={\mathrm{T}}_{\mathrm{i}}{\mathrm{CF}}_{\mathrm{i}}$$

(9)

$$\mathrm{PERI}={{\displaystyle \sum }}_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{E}}_{\mathrm{i}}$$

(10)

where Ei represents the monomial potential ecological risk factor; Ti represents the toxic response factor for a specified substance for which typical values are Cu = 5, Pb = 5, Mn = 1, Zn = 1 [31,32,33]; and CFi is the contamination factor for each metal under study.

Table 5. Classification for the Potential Ecological Risk Factor (E_i) [32].

Risk Factor Level	Ei Value
Low potential ecological risk	Ei < 40
Moderate potential ecological risk	40 ≤ Ei < 80
Considerable potential ecological risk	80 ≤ Ei < 160
High potential ecological risk	160 ≤ Ei < 320
Very high potential ecological risk	320 ≤ Ei

demonstrates the classification of resulting Ei values to show the overall effect of environmental contamination by the respective heavy metals.

3. Results and Discussion

3.1. Chemical Analysis

3.1.1. Descriptive Statistics

Concentrations for the heavy metals assessed in this study, as shown in decreasing order, include Sr > Fe > Mn > Pb > Zn > Cu, as ppm/g in the descriptive statistics shown in

Table 6. Descriptive statistics of heavy metal concentrations in sand.

Bay		Elemental Data (ppm/g)
		Cu	Fe	Mn	Pb	Sr	Zn
Ghadira	Mean	2.114	80.992	9.506	3.223	179.019	4.020
	Min	0.622	55.504	7.687	1.765	76.492	0.975
	Max	9.278	91.527	11.601	5.230	219.644	12.038
	SD	1.608	8.746	0.985	0.903	30.661	3.363
Rinella	Mean	9.561	116.575	12.098	21.300	263.098	8.710
	Min	7.977	114.849	11.410	20.607	235.524	8.526
	Max	10.783	117.621	13.039	22.119	286.957	8.829
	SD	1.438	1.506	0.843	0.764	25.917	0.161
Ramla	Mean	2.059	112.824	17.361	3.336	90.750	3.020
	Min	1.573	97.260	14.417	1.828	74.525	2.208
	Max	2.625	116.007	19.083	4.726	98.836	3.717
	SD	0.294	4.388	1.351	0.976	7.015	0.384
Mistra	Mean	5.771	85.054	7.692	8.665	118.955	5.007
	Min	4.164	74.517	7.574	7.499	110.500	4.190
	Max	7.377	95.590	7.811	9.831	127.410	5.823
	SD	2.272	14.901	0.168	1.649	11.957	1.154
White Tower	Mean	1.204	66.757	8.079	1.990	162.730	2.861
	Min	0.860	57.541	7.400	1.260	143.571	2.511
	Max	1.426	72.547	8.861	2.584	184.886	3.305
	SD	0.363	5.474	0.560	0.492	16.832	0.295
Little Armier	Mean	1.406	75.525	9.302	2.379	196.902	1.419
	Min	0.419	69.096	8.480	2.143	181.654	1.370
	Max	3.779	84.870	10.293	2.647	212.383	1.466
	SD	1.589	6.676	0.832	0.243	13.161	0.053
Xemija	Mean	2.693	89.334	7.550	4.663	98.836	3.623
	Min	2.693	89.334	7.550	4.663	98.836	3.623
	Max	2.693	89.334	7.550	4.663	98.836	3.623
	SD	N/A	N/A	N/A	N/A	N/A	N/A
Ballut Reserve	Mean	16.909	93.747	11.195	37.949	205.563	4.741
	Min	5.986	87.364	10.156	12.478	194.677	3.531
	Max	40.812	112.856	12.403	102.845	220.674	8.761
	SD	13.841	10.729	0.849	37.479	9.873	2.251
Xlendi	Mean	4.322	79.360	16.554	10.461	99.398	4.447
	Min	4.322	79.360	16.554	10.461	99.398	4.447
	Max	4.322	79.360	16.554	10.461	99.398	4.447
	SD	N/A	N/A	N/A	N/A	N/A	N/A
Paradise	Mean	1.536	79.897	11.254	3.545	152.940	6.388
	Min	1.482	70.565	10.704	3.025	147.834	5.656
	Max	1.584	85.204	12.022	4.663	160.997	7.092
	SD	0.046	6.523	0.553	0.757	5.721	0.600
Hondoq	Mean	1.460	74.284	6.252	2.300	56.748	3.237
	Min	1.109	50.862	5.657	1.765	54.242	2.567
	Max	1.810	97.706	6.848	2.836	59.254	3.907
	SD	0.496	33.124	0.842	0.758	3.544	0.947
Golden	Mean	1.235	106.333	12.133	2.192	147.319	6.013
	Min	0.430	92.596	10.557	1.575	85.158	0.990
	Max	3.417	113.992	13.477	2.647	180.717	10.229
	SD	0.752	6.788	0.947	0.332	34.855	3.433
St. George	Mean	2.385	90.119	7.251	2.883	364.892	1.952
	Min	1.313	79.070	5.433	2.395	334.596	1.711
	Max	4.549	103.617	8.701	3.844	407.624	2.183
	SD	1.464	10.317	1.424	0.670	32.920	0.193
Gnejna	Mean	2.237	110.587	12.925	3.371	93.847	5.661
	Min	1.279	106.678	10.594	2.080	85.392	2.155
	Max	3.836	114.849	14.875	6.869	103.380	16.049
	SD	0.826	2.252	1.509	1.358	6.577	5.265
Pretty	Mean	1.309	81.636	5.589	3.772	403.719	2.766
	Min	0.950	69.018	3.132	2.710	361.156	2.362
	Max	4.435	88.555	8.403	4.726	489.177	3.188
	SD	0.905	5.156	1.604	0.644	35.216	0.268
Riviera	Mean	1.291	116.558	11.987	3.926	159.891	10.155
	Min	0.871	109.261	8.192	3.277	144.274	7.286
	Max	1.641	121.061	14.530	5.420	179.640	12.823
	SD	0.279	4.060	2.168	0.645	9.962	1.814
Armier	Mean	0.685	87.704	5.562	3.984	229.340	2.130
	Min	0.226	75.809	2.363	2.458	204.373	1.622
	Max	2.037	97.516	7.570	8.886	262.177	3.026
	SD	0.522	6.347	1.858	1.934	24.846	0.525
Marsalforn	Mean	10.025	125.091	13.698	8.381	85.392	8.927
	Min	10.025	125.091	13.698	8.381	85.392	8.927
	Max	10.025	125.091	13.698	8.381	85.392	8.927
	SD	N/A	N/A	N/A	N/A	N/A	N/A
Skewness Coefficient		6.638	−0.119	0.322	8.293	1.241	1.368

. Background levels of heavy metal content for Malta are not yet available, thus comparison of values was not possible. Results have been compared to concentrations documented in other similar studies for locations around the Mediterranean Sea, including Italy, Spain, and Egypt [33,34,35,36], and to background worldwide carbonate rock values issued by Turekian and Wedepohl [31]. A comparison of results is documented in

Table 7. Concentrations of heavy metals in sand in different Mediterranean studies.

Element	Mean Concentrations in This Study (µg/g)	Study from Baia Domitia, Italy (ppm) [33]	Study from Malaga Bay, Spain (mg/Kg) [34]	Study from Abu Khashaba Beach, Egypt (µg/g) [35]	Study from Sabratha Coast, Libya (µg/g) [36]	Carbonate Rock Background Values (ppm) [31]
Cu	2.549	-	-	50–89	17.300	4
Fe	93.766	-	-	44,900–198,100	2084	3800
Mn	10.503	-	-	450–640	36.210	1100
Pb	5.082	23.5	46.7–218	214–476	11.690	9
Sr	186.953	-	-	-	-	610
Zn	4.474	52.5	-	53–388	26.550	20

below.

It can be observed that heavy metal concentrations obtained in this study are lower than background values documented by Turekian and Wedepohl [31] for carbonate sedimentary rocks. Values acquired are closest to those referenced in the Sabratha Coast in Libya, especially in the case of Pb, where samples were also analysed using AAS technology. Concentrations for sand in Maltese bays was notably lower than those recorded in all studies referenced in Table 7. Unfortunately, Sr content was not reviewed in any of these studies so comparison lacks, but the concentration obtained is much less than background value for Sr stated by Turekian and Wedepohl [31].

The distributions of Fe and Mn proved symmetrical since the skewness coefficients are almost equal to zero and the mean and median values are very close. This means that their distributions are homogenous and are derived from non-point sources which may be of predominant lithogenic origin, namely bedrock erosion due to wave dominant coastal location [33] or predominant anthropogenic sources which may include industrial pollution, vehicle emissions from nearby roads, tourist resorts, stormwater runoff [35], and contamination from sea vessel fuel hydrocarbon derivatives from marine traffic [36]. Skewness coefficients for Cu, Pb, Sr, and Zn are higher showing that data might be affected by point sources found on the specific beaches analysed. This is further studied by principal component analysis and cluster analysis.

Upon carrying out the Kolmogorov–Smirnov normality test on the concentrations for the heavy metals assessed, only the data for Mn are normal with a p = 0.200 > 0.050. This result links directly to the homogeneity of the Mn distribution shown by the skewness coefficient, and hints to the possibly prevalent lithogenic origin of Mn.

The Mann–Whitney U-test and the Kruskal–Wallis test, shown in

Table 8. Hypotheses assessed using Mann–Whitney U-test and Kruskal–Wallis test for sand beaches reviewed.

Elements	Hypothesis 1: Distribution is the Same across Locations.	Hypothesis 2: Distribution is the Same across Coordinates.	Hypothesis 3: Distribution is the Same across Bays.
Cu	Rejected	Rejected	Rejected
Fe	Rejected	Rejected	Rejected
Mn	Rejected	Rejected	Rejected
Pb	Retained (0.729)	Rejected	Rejected
Sr	Rejected	Rejected	Rejected
Zn	Retained (0.776)	Rejected	Rejected
Test significance level is 0.05.

, were performed to check relationships among the concentrations across the different islands of Malta and Gozo; across the coordinates where these lie, including the north, south, west or east region of Malta and Gozo; and across each of the bays reviewed. It is important to note that the Pb distribution resulted in the same in Malta and Gozo, with Mean Rank for Malta = 67.010; Mean Rank for Gozo = 70.280; N = 134; U = 1195.500; p = 0.729. The distribution of Zn concentrations was also similar over the two islands studied, with Mean Rank for Malta = 67.100; Mean Rank for Gozo = 69.780; N = 134; U = 1185.500; z = 0.284; p = 0.776. The hypotheses defining similarity across Malta and Gozo for all other elemental concentrations were rejected since the obtained values of p < 0.05.

None of the heavy metal concentrations were the same in different regions located around the coast, thus this hypothesis was rejected. Cu and Sr were highest in the south of Malta and Gozo, Zn and Fe had higher concentrations along the west coast, while Mn and Fe proved more abundant along the east coast. Upon performing a Kruskal–Wallis test, Pb concentrations were marginally equal in all regions (H(3) = 13.984; N = 134; p = 0.003), although a considerable number of outliers resulted from samples located in southern bays.

None of the elemental concentrations studied have been proven similar across the different bays chosen for this study. The highest concentrations of heavy metals were recorded in Rinella Bay in Malta and Marsalforn Bay in Gozo, followed by Ballut Reserve Bay and Riviera Bay in Malta, and Ramla Bay and Xlendi Bay in Gozo. Sr concentrations were mostly predominant in two bays located in the south-east region of Malta and which are in proximity: Pretty Bay and St. George’s Bay, and in Rinella Bay which is on the eastern Maltese coast. Most of the bays are close to highly industrialized zones: namely a boatyard, a freeport terminal, and a dockyard. Oils and fuel derivatives from neighbouring anthropogenic activity are highly likely the source for such concentrations. Other bays, namely Riviera Bay, Ramla Bay, and Xlendi Bay, are high touristic attractions, thus an increased level of human activity could be the source of high heavy metal content in such locations. High Sr and Fe content could also be attributed to lithogenic sources of such elements. Sr is predominantly sourced in natural minerals, namely celestite, strontianite, and gypsum, which is derived from the weathering of Coralline Limestone and precipitate from aqueous solutions to form sedimentary rocks [37]. Fe is also sourced in natural geology, namely sedimentary rock types including limestone, by forming different minerals in reaction to different environmental conditions [38]. This gives sand a red hue typical of Ramla Bay in Gozo.

3.1.2. Correlation Analysis

Spearman-Rho correlation analysis performed on the data obtained, illustrated in Figure 1, showed that the highest correlation was between Fe and Mn concentrations, with r = 0.682. This shows a strong linear relationship between these heavy metals, possibly due to them having a similar source. This corresponds to the homogeneity results obtained for Mn proving its predominant lithogenic origin. Both Fe and Mn content was also observed to be higher along the eastern coast of the islands, most probably originating from the natural degradation of sedimentary rock. In fact, the second-most prevailing winds for Malta and Gozo are south-easterly winds; thus, more sea waves tend to strike the islands from that direction, increasing the wear rate in those coordinates by noticeable factors [39]. The Mn and Sr relationship is also strong but negative (r = −0.639). The correlation factor of Sr with Fe is moderately high with r = −0.4. This proves that Sr also hints towards a lithogenic origin, as outlined in the Kruskal–Wallis test results described. Such results match those recorded in other studies, namely by El-Sorogy et al., where strong associations (r > 0.7) between Fe, Mn, and Sr were recorded [35], and by Nour and El-Sorogy, where the correlation between Mn and Pb was high (r = 0.816) [36].

Correlations between most of the other heavy metals assessed were moderate and in the range 0.2 < r < 0.5. Results from studies by El-Sorogy et al. [35] showed higher correlations than those obtained in this study.

Relationships between Sr and other elements assessed were negative, except for the link with Pb which was weak but positive with r = 0.118; this proved that while Sr concentration decreased, Pb concentration decreased with a marginally similar pattern. On the other hand, other elemental concentrations tend to increase while Sr concentration decreases, proving an inverse relationship. This outcome differs from that obtained by El-Sorogy et al. in their coastal sediment analysis performed in Egypt [35], but partly matches previously documented local work undertaken on agricultural soil in Malta [10].

3.1.3. Multivariate Statistical Analysis

Principal component analysis (PCA) has been used to reduce the number of variations without losing important data to understand relationships between the concentrations obtained for the different heavy metals contained in sand collected from various beaches reviewed in this study.

An eigenvalue of 2.2893 with 38.200% and a cumulative percentage of 38.200% for PC1 was obtained, and an eigenvalue of 1.4521 with 24.2% and a cumulative percentage of 62.4% for PC2 was achieved. Upon applying factor analysis, two factors were retained by the number of factor criterion using the Maximum Likelihood/Varimax rotations, where a variance of 1.878 with 31.295% and a cumulative percentage of 31.295% was obtained for Factor 1, and a variance of 0.933 with 15.554% and a cumulative percentage of 46.849% was achieved for Factor 2, as documented in

Table 9. PCA initial factor loadings matrix and rotated factor loadings matrix.

	PC1	PC2	F1	F2
Cu	0.605	0.468	0.359	0.676
Fe	0.780	−0.099	0.752	0.230
Mn	0.879	−0.265	0.910	0.120
Pb	0.226	0.822	−0.132	0.842
Sr	−0.678	0.487	−0.819	0.164
Zn	0.600	0.286	0.429	0.507

below.

Strong effects can be identified for the elements with the higher loadings under PC1 for which only Pb content seems to have less of an effect from such influences. Effects on Pb concentrations were highlighted as dominant under PC2, which matches results issued by the rotated factor loadings under F1 and F2 in Table 9. This could possibly indicate that Cu, Fe, Mn, Sr, and Zn have a prevalent lithogenic origin, while Pb content could be affected more by anthropogenic sources. Upon observation of factor loadings, it can be noted that Cu and Zn may also be attributed to having anthropogenic relation, and thus such elemental concentrations in the sand could originate from mixed sources—natural and human-induced.

A biplot (Figure 2a), a principal component loadings plot (Figure 2b), and a 3-D scatterplot (Figure 2c) are shown in Figure 2. This shows that the data obtained could be grouped into four main clusters, depicted by the differently coloured data points. Most concentrations of the elements studied obtained from the different locations can be grouped but some outliers can be easily identified. In-depth analysis of such outlier data points showed that these results were obtained from samples collected close to areas of higher human activity, namely food tills, restaurants or kiosks, and renting tills for beach leisure and water sport activities. At these sampling points, higher levels of Pb and Cu were also noted. This result matches that obtained in the FA applied, from which it could be concluded that Pb and Cu could be partly attributed to anthropogenic origin. This requires further scientific analysis to outline the real effects of such human activities on local beaches and their long-term effects on beach life, both in terms of pollution and health hazards to natural resources and human life.

The clusters which resulted from the biplot and PCA scatterplot were further scrutinised by applying cluster analysis. CA was used to group similar concentrations obtained across sampling points across all bays analysed, as illustrated in Figure 3.

Four main groups have been identified, matching results gained through conducting the PCA and FA. From the heat map included, the first cluster shows that a large number of samples had similar concentrations of Fe, Mn, and Sr, while it can be noted that an outlier for Mn and a couple for Zn were present at samples collected from St. George’s Bay. The second cluster was mapped on fewer number of samples and presented all the samples that had higher Sr content. The sampling points included in this cluster are those located at Pretty Bay and St. George’s Bay, verifying a previously discussed outcome. The third cluster links samples from Rinella Bay depicting moderate to high concentrations for all the elements reviewed. The higher concentrations were mapped for Fe, Cu, and Mn for samples gathered from Rinella Bay, while the lowest concentration identified in this group is that for Sr content found at Marsalforn Bay in Gozo. The fourth cluster identified contained a large number of samples and grouped mostly high concentrations of Fe, Mn, and Zn across different sampling points at Gnejna Bay, Golden Bay, Riviera Bay in Malta, and Ramla Bay in Gozo. The lowest concentrations were mapped for Pb content at Golden Bay. It is important to note that these mentioned bays are highly popular with locals and tourists and are of the largest sand beaches on the islands. This proves that mixed lithogenic and anthropogenic origin of these elements is highly possible, especially at these extremely visited beaches at particular locations highlighted by the respective sampling points along the transects taken on these beaches.

3.2. Environmental and Health Risk Assessment

3.2.1. Health Risk Assessment

Hazard Quotient (HQ), Hazard Index (HI), Cancer Risk (CR), and Risk Index (RI) values computed for the analysed data are tabulated in

Table 10. Results for the Health Risk Assessment and the Potential Ecological Risk Assessment variables.

Element	Mean Concentration (ppm/g)	HQdefault	HQmodified	CRdefault	CRmodified	Igeo	CF	Ei
Cu	2.549	1.092 × 10−3 *	1.28 × 10−4 *	N/A	N/A	−1.268	0.947	85.252
		1.600 × 10−2 **	1.872 × 10−3 **	N/A	N/A
Fe	93.766	3.680 × 10−4 *	4.310 × 10−5 *	N/A	N/A	−5.962	0.024	N/A
		8.450 × 10−4 **	1.154 × 10−3 **	N/A	N/A
Mn	10.503	4.780 × 10−4 *	5.596 × 10−5 *	N/A	N/A	−6.056	0.042	0.765
		5.479 × 10−3 **	6.420 × 10−4 **	N/A	N/A
Pb	5.082	1.451 × 10−2 *	1.700 × 10−3 *	4.319 × 10−7 *	5.060 × 10−8 *	−1.509	0.793	71.600
		4.609 × 10−2 **	6.300 × 10−2 **	1.372 × 10−6 **	1.875 × 10−6 **
Sr	186.953	7.950 × 10−4 *	9.320 × 10−5 *	4.274 × 10−13 *	4.975 × 10−14 *	−2.597	0.282	N/A
		1.825 × 10−3 **	2.494 × 10−3 **	1.332 × 10−12 **	9.743 × 10−13 **
Zn	4.474	2.222 × 10−4 *	2.600 × 10−5 *	N/A	N/A	−2.863	0.236	4.404
		3.406 × 10−3 **	3.990 × 10−4 **	N/A	N/A
HIadults	Default = 0.018; Modified = 0.002
HIchildren	Default = 0.074; Modified = 0.070
RIadults	Default = 4.320 × 10−7; Modified = 5.060 × 10−8
RIchildren	Default = 1.372 × 10−6; Modified = 1.875 × 10−6
PERI	162.021

HQ represents the Hazard Quotient; CR represents the Cancer Risk values; Igeo represents the geo-accumulation index for each heavy metal; CF represents the carcinogenic factor; Ei represents the Potential Ecological Risk Index. * Values computed for Adults. ** Values computed for Children.

. Non-carcinogenic risks represented by HQ values calculated for adults by ingestion and dermal contact proved not to cause risk to human health, as these lie in the range < 0.1. Similar HQ values computed for children are higher than those calculated for adults but still fall under the no risk range. The Hazard Index for adults based on the non-carcinogenic hazard quotient values show a negligible effect for adults with HI = 0.018, and negligible non-carcinogenic effects for children as the resulting HI = 0.074 < 0.1. Distributions for the HI for children across the bays analysed are illustrated in Figure 4. Similar results were noted for adults. The highest HI were recorded in Ballut Reserve Bay for Cu and Pb, in Marsalforn Bay for Mn, and in Riviera Bay for Zn, while approximately equal HI values were reported for Fe across all bays. Different reasons account for such results; tying in with the results obtained from the statistical analysis. Some of the bays that have reported high effects of these heavy metals are located close to industrial zones, while other sources could be more typical of the origin of the rocks.

CR values could only be worked out for Pb and Sr. with the results indicating a very low cancer risk for adults since RI for adults and children <10⁻⁶. This is non-detrimental to the environment and society but is a result which must be further analysed to be able to suggest actions to contain and upkeep such contamination levels on local beaches. Containment actions and awareness of the sources of such pollutants is important to address toxicity on such sand beaches.

Modified results for HQ and CR values obtained were in the same order as those computed through the default values used across different studies. These modified values were calculated to provide a better approximation of the exposure frequency of persons visiting the studied sites. Such results are tabulated in Table 10.

3.2.2. Potential Ecological Risk Assessment

Values for the geo-accumulation index, I_geo, calculated for each of the heavy metals assessed across all bays, were negative indicating that the beaches analysed are uncontaminated from heavy metals. The ecological risk factor, E_i, shows that a potential ecological risk may originate from Cu and Pb concentrations upon comparison with the background levels associated with these elements. Such results are tabulated in Table 10.

With reference to range values outlined in Table 4, Cu tends to cause a considerable potential ecological risk, while Pb poses a moderate potential ecological risk. E_i values computed for Mn and Zn are low and thus these elements present a low ecological risk. Figure 5 illustrates the distributions of the monomial potential ecological risk factor computed per bay studied. It can be observed that Ballut Reserve Bay, Marsalforn Bay, and Rinella Bay have the highest risk factor for Cu; Marsalforn Bay, Riviera Bay, and Gnejna Bay and Golden Bay (equal percentage risk) have the highest risk for Mn; Ballut Reserve Bay, Rinella Bay, and Marsalforn Bay and Mistra Bay (equal percentage risk) have the highest risk factor for Pb; and Riviera Bay, and Rinella Bay and Marsalforn Bay (equal percentage risk) have the highest risk factor for Zn content. This shows that the beaches with a higher overall potential ecological risk were Ballut Reserve Bay, Marsalforn Bay, and Rinella Bay. Marsalforn Bay is located in the north-east part of Gozo, while Ballut Reserve Bay and Rinella Bay are located in the south-east region of Malta. This confirms that both lithogenic and anthropogenic sources affect the elemental concentrations reviewed, since some of the bays outlined are located in the same direction as the prevalent winds on the islands and most are highly frequented beaches.

4. Conclusions

Results obtained in this study provide a greater insight into heavy metal pollution in sand beaches located around the islands of Malta and Gozo. None of the concentrations documented were higher than those reported by other studies in the Mediterranean region. The highest heavy metal concentrations were listed for Ballut Reserve Bay, Rinella Bay, Riviera Bay, Marsalforn Bay, and Ramla Bay. The highest number of outlier data points resulted from Ballut Reserve Bay, Ghadira Bay, and Ramla Bay. Pb and Zn content results were similar across different locations but none of the concentrations documented were the same across bays or across different coordinates. Outlier data points on different beaches were located close to human activity zones, which included food tills and restaurants, water sport and leisure equipment renting tills, and road runoff.

A strong correlation between Fe and Mn was recorded, hinting towards a prevalent lithogenic origin of the two elements, possibly from rock erosion by sea waves, especially along the east coast of the islands where prevailing winds blow.

Multivariate analysis results showed that Pb is possibly obtained from highly anthropogenic sources, while Cu and Zn have a mixed origin. Fe, Mn, and Sr are of a lithogenic origin. Cluster analysis grouped sampling points into four main groups gathered according to the concentration levels of the heavy metals studied across different bays. High Sr content was related to samples collected from Pretty Bay and St. George’s Bay; higher Fe, Cu, Mn, and Zn content was linked to Rinella Bay, Golden Bay, Gnejna Bay, Riviera Bay, and Ramla Bay; the lowest Pb content was related to Golden Bay.

Such results tie in with the health risk assessment and potential ecological risk assessment results obtained. Very low Hazard Index and Cancer Risk Index values for adults and children resulted from the calculations performed. Thus, persons frequenting beaches are not at a risk of non-carcinogenic and carcinogenic effects by ingesting and having long-term exposure to such heavy metals present in sand beaches. Nonetheless, although the beaches studied are not contaminated overall, a considerable ecological risk of contamination is present because of Cu content and a moderate ecological risk is caused by Pb content found mainly in Ballut Reserve Bay, Rinella Bay, and Marsalforn Bay.

Further scientific analysis is suggested to assess these outcomes in detail and make the necessary decisions to minimise toxicity levels on sand beaches to reduce the ecological risk.